class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def _transform(self, raw_documents, is_fit):
      documents = [document.split() for document in raw_documents]
      vocab = sorted(set((reduce(lambda x, y: x + y, documents))))
      max_length_doc = max(len(_) for _ in documents)
      if is_fit:
          self.max_length = max(self.max_length, max_length_doc)
          self._update_vocab(vocab)
      output = [self._doc2index(document) for document in documents]
      return output
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 5 for calling the WordVectorTransformer obj's underlying member method code with example usage: obj._transform(raw_documents, is_fit) and returns: output